aimlroles

Machine Learning Engineer

DEKA Research & Development dekaresearch.com Open · verified Oct 22, 2026
Manchester Full-time Mid-level Machine Learning Engineering

At a glance

Mid-level Machine Learning Engineering role at DEKA Research & Development. Manchester · full-time · no salary stated · we estimate $142,875–$228,150 across the United States.

Estimated pay

DEKA Research & Development has not published a salary for this role. The figure below is our estimate, not theirs.

$142,875 – $228,150
What the middle half of mid-level machine learning engineering roles in the United States paid, among postings that did state a salary.
Worked out from 284 postings that stated pay, median $186,750. Last updated Oct 22, 2026.
RoleMachine Learning Engineering
SeniorityMid-level
LocationManchester
EmploymentFull-time
PostedAug 27, 2026 · 8w ago
Role brief

aimlroles summary, based on the employer's posting.

What you'll do

  • Build backend services and microservices for applications that handle substantial data and real-world use
  • Take ML models from feature preparation and training through evaluation, launch, and ongoing monitoring
  • Create and maintain event-driven data pipelines with Kafka, Spark, and related technologies
  • Connect ML models to rule-based decision engines for automated decisions in real time
  • Work across disciplines on engineering solutions, including AI and LLM workflow orchestration

What you bring

  • A master’s degree in computer science, AI, or a related discipline
  • At least six years working in backend engineering and distributed systems
  • Strong Java and Python skills, plus practical experience deploying production ML models
  • Experience with Kafka, Spark, and ZooKeeper, alongside microservices and API design

Who this fits

This mid-level, full-time role suits an engineer who works across backend systems and applied machine learning. It is on-site in Manchester, New Hampshire, and requires you to be local; relocation is unavailable.

From the employer

We're looking for a Machine Learning Engineer who can operate at the intersection of backend engineering and applied machine learning. If you want to design distributed systems, deploy production ML models, and architect scalable data pipelines that make a measurable difference — this is your opportunity, and it starts with the software you design and deliver at DEKA.

This role is on-site at DEKA in Manchester, NH. Relocation is not available, local candidates only.

How You Will Make an Impact:
  • Design and implement scalable backend services and microservices powering data-intensive, real-world applications
  • Build and deploy production ML models across the full lifecycle from feature engineering and training through evaluation, deployment, and monitoring
  • Develop and maintain event-driven distributed pipelines using Apache Kafka, Apache Spark, and related technologies
  • Architect systems that integrate ML models with rule-based decision engines for automated, real-time decisioning
  • Collaborate across disciplines to translate complex requirements into reliable, elegant engineering solutions
  • Contribute to AI/LLM-driven workflows and orchestration systems that push the boundaries of what software can do
Required Experience/Knowledge:
  • M.S. in Computer Science, AI, or a related field
  • 6+ years in backend software engineering and distributed systems
  • Strong proficiency in Java (Spring Boot, Spring MVC, Hibernate) and Python
  • Hands-on experience building and deploying production ML models (PyTorch or equivalent)
  • Experience with distributed systems and streaming technologies: Apache Kafka, Apache Spark, ZooKeeper
  • Solid understanding of microservices architecture, REST/SOAP APIs, and object-oriented design
  • Experience with relational and NoSQL databases (Oracle, IBM Db2, MSSQL, MongoDB)
  • Familiarity with AWS or equivalent cloud platforms
  • Strong problem-solving skills
  • Intrinsic drive to understand how things work and make them better
  • Excellent communicating and collaboration across engineering, data, and product teams
  • Strong attention to detail